Anomaly Detection in PowerCells Auxiliary Power Unit

dc.contributor.authorHjortberg, Hampus
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.description.abstractIn the paper of Hayton [1], One-class Support Vector Machine is used for health monitoring of a jet engine in order to discover when and if an abnormal event has occured. Hayton used the amplitude of the vibration data from the engine shaft as the feature data to the One-class Support Vector Machine algorithm. This approach works well when the sensor data is known to be periodic, with a certain frequency; however it can not be used if the sensor data has an irregular shape. In this paper we will extend the concept of Hayton [1] and use the Discrete Wavelet Transform coefficients as input data to the OCSVM, rather than the Fourier Transform. This way we are able to classify more arbitrary sensor data found in PowerCells Auxilliary Power Unit (APU). We will also introduce a novel approach of how to select the hyperparameter s for the Radial Basis Function Kernel, in order to avoid both overfitting and underfitting.
dc.subjectInformations- och kommunikationsteknik
dc.subjectData- och informationsvetenskap
dc.subjectInformation & Communication Technology
dc.subjectComputer and Information Science
dc.titleAnomaly Detection in PowerCells Auxiliary Power Unit
dc.type.degreeExamensarbete för masterexamensv
dc.type.degreeMaster Thesisen
local.programmeComplex adaptive systems (MPCAS), MSc
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